Ahmad, S.; Brink, T.; Liebscher, C.; Dehm, G.: Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt boundaries in aluminum. Acta Materialia 268, 119732 (2024)
Brink, T.; Langenohl, L.; Ahmad, S.; Liebscher, C.; Dehm, G.: Atomistic Modeling of the Thermodynamics of Grain Boundaries in fcc Metals. 19th International Conference on Diffusion in Solids and Liquids, Crete, Greece (2023)
Ahmad, S.; Liebscher, C.; Dehm, G.: To decipher the novel atomic structure of [111] tilt grain boundaries in Al. Material Science and Engineering Congress - MSE 2020, virtual, Darmstadt, Germany (2020)
Saood, S.; Liebscher, C.; Dehm, G.: Observing the atomic structure of high angle [111] tilt grain boundaries in Al. Materials Science and Engineering Congress MSE 2020, virtual (2020)
Saood, S.; Brink, T.; Liebscher, C.; Dehm, G.: Atomic structure of [111] tilt boundaries of Al in relation to their crystallographic parameters. International Microscopy Conference 2023 (IMC-20), Busan, South Korea (2023)
Ahmad, S.; Liebscher, C.; Dehm, G.: Exploration of atomic structures in Σ3 [111] Al tilt grain boundaries. Sixth Conference on Frontiers of Aberration Corrected Electron Microscopy PICO 2021, virtual, Kasteel Vaalsbroek, The Netherlands (2021)
Ahmad, S.; Liebscher, C.; Dehm, G.: Strain-Induced phase transition in Σ3 [111] (211) tilt grain boundaries in Al. Microscopy conference Joint Meeting of Dreiländertagungn & Multinational Congress on Microscopy MC 2021, virtual, Vienna, Austria (2021)
Ahmad, S.; Meiners, T.; Frolov, T.; Liebscher, C.; Dehm, G.: Grain boundary structure and phase transitions in Cu and Al [111] tilt grain boundaries. International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, IAMNano, Düsseldorf, Germany (2019)
Ahmad, S.: Fundamental investigation of the atomic structures of [111] tilt grain boundaries, their defects and segregation behaviour in pure and alloyed Al. Dissertation, Ruhr-Universität Bochum (2023)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…